GitHub6d
Machine Learning for .NET
ML.NET is a cross-platform open-source machine learning (ML) framework for .NET ... With ML.NET, you can train models for a variety of scenarios, like classification, forecasting, and anomaly ...
marktechpost23d
SHREC: A Physics-Based Machine Learning Approach to Time Series Analysis
Reconstructing unmeasured causal drivers of complex time series from observed response data represents ... causal driver reconstruction usually rely on signal processing or machine learning frameworks ...
C&EN25d
Integrating C–H Information to Improve Machine Learning Classification Models for Microplastic Identification from Raman Spectra
Department of Chemistry, University of Waterloo, 200 University Avenue W., Waterloo, Ontario N2L 3G1, Canada Waterloo Institute for Nanotechnology, University of Waterloo, 200 University Avenue W., ...
IEEE27d
A Deep Learning-Based Approach for Two-Phase Flow Pattern Classification Using Void Fraction Time Series Analysis
Abstract: Flow regime classification is essential for analyzing and modeling two-phase flows, as it demarcates the flow behavior and influences the selection of appropriate predictive models. Machine ...
IEEE1mon
FedDict: Towards Practical Federated Dictionary-based Time Series Classification
Abstract: The dictionary-based approach is one of the most representative types of time series classification (TSC) algorithm due to its high accuracy, efficiency, and good interpretability. However, ...
GitHub8mon
machine-learning-pipeline
This project is focused on the Deployment phase of machine learning. The Docker and FastAPI are used to deploy a dockerized server of trained machine learning pipeline. Attendance prediction tool for ...